1. Field of the Invention
The present invention generally relates to a method and system for estimating perceived overall lightness contrast for a digital image and more particularly to a process and system that combines the following five computed measures: image edge contrast; range of lightness; area contrast; average lightness of an image, relative to the viewing background; average lightness, relative to the pivot point of the tone reproduction curve in computing overall lightness contrast.
2. Description of the Related Art
There are a wide range of conventional models and systems that calculate the contrast of an image. For example, some models, that numerically predict global contrast for complex patterns or natural images were suggested by Lubin (J. Lubin A Visual Discrimination Model for Imaging System Design and Evaluation, In: Vision Models for Target Detection, World Scientific, Singapore, 1995), Peli (E. Peli. Contrast of Slightly Complex Patterns: Computing the Perceived Contrast of Gabor Patches In: Human Vision and Electronic Imaging, SPIE., 1996, v.2657, 166–174), and Moulden (B. Moulden, et al., The Standard Deviation of Luminance as a Metric for Contrast in Random-dot Images Perception, 1990, v. 19, n. 1, p.79), all of which are incorporated herein by reference.
Peli's model and its modification by Lubin require estimation of the local contrast for every pixel within several different frequency bands. A combination rule to produce a local measure of contrast across the bands is based on Pythagorean summation. Although this measure was applied to the case of supra-threshold contrast for a Gabor pattern, it has not been conventionally shown how this can be used to assess overall perceived contrast for a complex natural image.
Moulden disclosed the use of a measure of the standard deviation of luminance to predict contrast for a specific type of images: random-dot images. The standard deviation of luminance produces an imprecise estimation of perceived contrast for black-and white images of natural scenes (coefficient of correlation is less than 0.57 according to a recent evaluation).
U.S. Pat. No. 4,731,671, incorporated herein by reference, describes a method for contrast estimation of an image for the subsequent contrast adjustment in digital image processing. In this method the contrast is automatically determined as a function of the standard deviation of a sample of tone values used to generate the tone reproduction function by normalizing a histogram of the sample of tone values. The standard deviation is used as an estimate of the scene contrast because of the correlation between the standard deviation and the scene contrast. That is, a high standard deviation corresponds to a high scene contrast and a low standard deviation corresponds to a low scene contrast.
The estimated contrast is then compared with a distribution of scene contrasts (i.e., standard deviations from a plurality of scenes) pre-computed from a plurality of random sample of images. If the estimated contrast is higher than the population average, then the image is considered to have a higher than normal contrast and the system reproduction contrast is then adjusted lower so that the printed image will have an image contrast that is closer to the average contrast. If the estimated image contrast is lower than the average, then the system reproduction contrast is raised accordingly.
Although this method works satisfactory for adjusting tone scale in many digital images, it has several shortcomings. Notably, it does not exclude noise and textures, which causes the standard deviation of the selected histogram and, hence, contrast estimation to be biased by large uniform areas or by busy texture areas (such as grass or trees). Secondly, the selected histogram often exhibits bimodality for overcast scenes with sky in them. Despite the scene contrast being low, the standard deviation of the selected histogram is large because of the bimodality caused by the dark grass pixels and the bright sky pixels.
Further, U.S. Pat. No. 5,822,453, incorporated herein by reference, suggested an improved method for estimating contrast of a digital image by using standard deviation of the sampled histogram of the relative log exposure values in an image as a measure of the scene contrast. Sampling of the image histogram ensures that textures and noise in large uniform areas do not affect computation of the scene contrast. The method creates a Laplacian histogram distribution. From the Laplacian histogram it then determines two thresholds, which eliminate substantially uniform areas or a substantially textured portion of the digital image. Based on these thresholds, pixels are selected from the digital image to form a sampled histogram. The standard deviation of the sampled histogram is then computed and used as a measure of the scene contrast. The estimation of contrast of the digital image is performed by comparing the computed standard deviation with a predetermined contrast for determining contrast of the input image in relationship with the predetermined contrast.
Experimental evaluation of this measure shows that it provides higher precision for the assessment of contrast for black-and-white images presented on a screen of the monitor, in comparison to the methods described before (correlation—0.69). However, this method does not address the problem that the scene contrast is affected by the image structure which includes edges, areas of certain lightness values, range of lightness, and other features and is, therefore, a multidimensional percept.
Similarly, U.S. Pat. No. 5,642,433, incorporated herein by reference, discusses a method for single image based image contrast quality evaluation in an automated optical system, particularly in an automated biological screening system. This method emphasizes an importance of edges in an image for detection purposes, especially in images of cytological specimens. It identifies edges in an image using an edge detection technique, and then, computes an image contrast score as the ratio of the accumulated edges of edges intensity to the accumulated edge intensity. This method, however, is best suitable for specific images where detection of particular objects such as biological cells is required. It concentrates on the image edges and does not consider other contrast-related features that are important when a plurality of images of natural scenes are compared with respect to scene contrast.
Consequently, a need exists for an improved method for estimating the scene contrast as perceived by the observer which would serve as a criterion for adjusting the reproduced image contrast.
The invention described below provides higher precision for the assessment of contrast (correlation with the perceived contrast of 0.79–0.92 for different image sets tested). The invention is designed to achieve a reliable prediction for perceived image contrast for a variety of images of natural scenes. Additionally, the invention provides an estimation of a plurality of the contrast-related features which sum up to generate an overall contrast measure. This information can be used to selectively adjust only specific contrast-related features for any single image to satisfy individual preferences or compensate for a lack of contrast generated by other features.